Regression Testing of Database Applications Under an Incremental Software Development Setting

Abstract

Software regression testing verifies previous features on a software product when it is modified or new features are added to it. Because of the nature of regression testing it is a costly process. Different approaches have been proposed to reduce the costs of this activity, among which are: minimization, prioritization, and selection of test cases. Recently, soft computing techniques, such as data mining, machine learning, and others have been used to make regression testing more efficient and effective. Currently, in different contexts, to a greater or lesser extent, software products have access to databases (DBs). Given this situation, it is necessary to consider regression testing also for software products such as information systems that are usually integrated with or connected to DBs. In this paper, we present a selection regression testing approach that utilizes a combination of unsupervised clustering with random values, unit tests, and the DB schema to determine the test cases related to modifications or new features added to software products connected to DBs. Our proposed approach is empirically evaluated with two database software applications in a production context. Effectiveness metrics, such as test suite reduction, fault detection capability, recall, precision, and the F-measure are examined. Our results suggest that the proposed approach is enough effective with the resulting clusters of test cases.